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import webdataset as wds
from pathlib import Path
import json
import numpy as np
from PIL import Image
def main(
src_json,
dest_folder,
num_samples_per_tar=10000,
number_of_jobs=10,
job_offset=0,
):
with open(src_json, "r") as f:
data = json.load(f)
import pandas as pd
root_path = Path(src_json).parent
# Convert images list to pandas dataframe
data_df = pd.DataFrame(data["images"])
if "annotations" in data:
has_annotations = True
annotations_df = pd.DataFrame(data["annotations"])
# Join the dataframes on id to get category_id from annotations
data_df = data_df.merge(
annotations_df[["id", "category_id"]],
left_on="id",
right_on="id",
how="left",
)
categories_df = pd.DataFrame(data["categories"])
data_df = data_df.merge(
categories_df[
[
"id",
"name",
"common_name",
"supercategory",
"kingdom",
"phylum",
"class",
"order",
"family",
"genus",
"specific_epithet",
]
],
left_on="category_id",
right_on="id",
how="left",
)
data_df.rename(
columns={
"id_x": "id",
},
inplace=True,
)
del data_df["id_y"]
else:
has_annotations = False
data_df = data_df[data_df["latitude"].notna() & data_df["longitude"].notna()]
num_samples = len(data_df)
num_total_tar = num_samples // num_samples_per_tar + (
1 if num_samples % num_samples_per_tar > 0 else 0
)
number_of_tar_per_job = num_total_tar // number_of_jobs
if job_offset == number_of_jobs - 1:
data_df = data_df.iloc[
number_of_tar_per_job * job_offset * num_samples_per_tar :
]
else:
data_df = data_df.iloc[
number_of_tar_per_job
* job_offset
* num_samples_per_tar : number_of_tar_per_job
* (job_offset + 1)
* num_samples_per_tar
]
print(f"Processing job {job_offset} with {len(data_df)} / {num_samples} samples")
print(f"Number of tar: {number_of_tar_per_job} / {num_total_tar}")
print(f"Start shard: {number_of_tar_per_job * job_offset}")
with wds.ShardWriter(
str(Path(dest_folder) / "%04d.tar"),
maxcount=num_samples_per_tar,
start_shard=number_of_tar_per_job * job_offset,
) as sink:
for i in range(len(data_df)):
row = data_df.iloc[i]
image_path = Path(root_path) / Path("images") / row["file_name"]
dinov2_embedding_path = (
Path(root_path)
/ Path("embeddings")
/ Path("dinov2")
/ f"{row['file_name'].replace('.jpg', '.npy')}"
)
sample = {
"__key__": str(row["id"]),
"jpg": Image.open(image_path).convert("RGB"),
"dinov2_vitl14_registers.npy": np.load(dinov2_embedding_path),
"json": row.to_dict(),
}
sink.write(sample)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--src_json", help="pixel_input_folder")
parser.add_argument("--dest", help="path to destination web")
parser.add_argument(
"--num_samples_per_tar",
help="number of samples per tar",
type=int,
default=10000,
)
parser.add_argument("--number_of_jobs", help="number of jobs", type=int, default=10)
parser.add_argument("--job_offset", help="job offset", type=int, default=0)
args = parser.parse_args()
dest = Path(args.dest)
dest.mkdir(exist_ok=True, parents=True)
main(
args.src_json,
args.dest,
args.num_samples_per_tar,
args.number_of_jobs,
args.job_offset,
)
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